@inproceedings{b7a688b309074db883cd62357572a910,
title = "Sampled-data Synchronization of Recurrent Neural Networks with Multi-GPUs",
abstract = "This paper investigates the multi-rate sampled-data synchronization problem for recurrent reural networks with the multi-GPUs which have each different variable sampling rate. To handle the multi-GPU system with multi-sampling rate, the sampled-data sychronization error system is expressed as a summation of feedback subsystems with multi-sampling intervals. For the sampled-data controller design, Lyapunov functions with looped functions are constructed to use the information of the multi-rate sampling, and the modified free-matrix inequality is exploited to estimate the tighter upper bound intergral term. Finally, the simulation results show the effectiveness of the proposed method.",
keywords = "LMIs, Recurrent neural networks, Sampled-data, Synchronization",
author = "Yongsik Jin and Seungyong Han and Jongcheon Park and Lee, \{S. M.\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 ; Conference date: 06-12-2019 Through 09-12-2019",
year = "2019",
month = dec,
doi = "10.1109/SSCI44817.2019.9002962",
language = "English",
series = "2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2172--2177",
booktitle = "2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019",
}